Localization in Reconfigurable Intelligent Surface Aided mmWave Systems: A Multiple Measurement Vector Based Channel Estimation Method
Li, Kunlun; He, Jiguang; El-Hajjar, Mohammed; Yang, Lie-Liang (2024-04-24)
IEEE
24.04.2024
K. Li, J. He, M. El-Hajjar and L. -L. Yang, "Localization in Reconfigurable Intelligent Surface Aided mmWave Systems: A Multiple Measurement Vector Based Channel Estimation Method," in IEEE Transactions on Vehicular Technology, vol. 73, no. 9, pp. 13326-13339, Sept. 2024, doi: 10.1109/TVT.2024.3393022
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© 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202409175890
https://urn.fi/URN:NBN:fi:oulu-202409175890
Tiivistelmä
Abstract
The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS-assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.
The sparsity of millimeter wave (mmWave) channels in the angular and temporal domains is beneficial to channel estimation, while the associated channel parameters can be utilized for localization. However, line-of-sight (LoS) blockage poses a significant challenge on the localization in mmWave systems, potentially leading to substantial positioning errors. A promising solution is to employ reconfigurable intelligent surface (RIS) to generate the virtual line-of-sight (VLoS) paths to aid localization. Consequently, wireless localization in the RIS-assisted mmWave systems has become the essential research issue. In this paper, a multiple measurement vector (MMV) model is constructed and a two-stage channel estimation based localization scheme is proposed. During the first stage, by exploiting the beamspace sparsity and employing a random RIS phase shift matrix, the channel parameters are estimated, based on which the precoder at base station and combiner at user equipment (UE) are designed. Then, in the second stage, based on the designed precoding and combining matrices, the optimal phase shift matrix for RIS is designed using the proposed modified temporally correlated multiple sparse Bayesian learning (TMSBL) algorithm. Afterwards, the channel parameters, such as angle of reflection, time-of-arrival, etc., embedding location information are estimated for finally deriving the location of UE. We demonstrate the achievable performance of the proposed algorithm and compare it with the state-of-the-art algorithms. Our studies show that the proposed localization scheme is capable of achieving centimeter level localization accuracy, when LoS path is blocked. Furthermore, the proposed algorithm has a low computational complexity and outperforms the legacy algorithms in different perspectives.
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